Overview

Dataset statistics

Number of variables23
Number of observations863
Missing cells62
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory161.8 KiB
Average record size in memory192.0 B

Variable types

Numeric19
Categorical4

Alerts

route has constant value ""Constant
file is highly imbalanced (60.2%)Imbalance
posicao_acelerador_d has 31 (3.6%) missing valuesMissing
posicao_acelerador_e has 31 (3.6%) missing valuesMissing
distancia_percorrida has unique valuesUnique
distancia_percorrida_total has unique valuesUnique
velocidade_media has unique valuesUnique
velocidade_media_gps has unique valuesUnique
latitude_gps has unique valuesUnique
longitude_gps has unique valuesUnique
aceleracao has 21 (2.4%) zerosZeros
espaco_livre_no_tanque_combustivel has 30 (3.5%) zerosZeros
posicao_acelerador_d has 109 (12.6%) zerosZeros
posicao_acelerador_e has 280 (32.4%) zerosZeros
velocidade_gps has 52 (6.0%) zerosZeros

Reproduction

Analysis started2023-11-20 02:09:35.644004
Analysis finished2023-11-20 02:10:24.029980
Duration48.39 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

aceleracao
Real number (ℝ)

ZEROS 

Distinct843
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43031398
Minimum-2.195709
Maximum1.7882713
Zeros21
Zeros (%)2.4%
Negative100
Negative (%)11.6%
Memory size13.5 KiB
2023-11-19T23:10:24.165980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.195709
5-th percentile-0.51442719
Q10.19212421
median0.44711931
Q30.76655646
95-th percentile1.1708216
Maximum1.7882713
Range3.9839803
Interquartile range (IQR)0.57443225

Descriptive statistics

Standard deviation0.49710707
Coefficient of variation (CV)1.1552194
Kurtosis2.4654251
Mean0.43031398
Median Absolute Deviation (MAD)0.264871
Skewness-0.87700702
Sum371.36096
Variance0.24711543
MonotonicityNot monotonic
2023-11-19T23:10:24.301584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
2.4%
0.8424463018 1
 
0.1%
0.5837752376 1
 
0.1%
0.3203264004 1
 
0.1%
1.252345248 1
 
0.1%
0.4529950021 1
 
0.1%
0.9040016685 1
 
0.1%
0.5296592552 1
 
0.1%
0.6962866607 1
 
0.1%
1.086074837 1
 
0.1%
Other values (833) 833
96.5%
ValueCountFrequency (%)
-2.195708959 1
0.1%
-1.716341311 1
0.1%
-1.709490311 1
0.1%
-1.619495832 1
0.1%
-1.550757067 1
0.1%
-1.496720256 1
0.1%
-1.346840765 1
0.1%
-1.294101397 1
0.1%
-1.187086336 1
0.1%
-1.088056324 1
0.1%
ValueCountFrequency (%)
1.788271292 1
0.1%
1.653141279 1
0.1%
1.489512484 1
0.1%
1.482490574 1
0.1%
1.427069018 1
0.1%
1.402457273 1
0.1%
1.397839449 1
0.1%
1.389035906 1
0.1%
1.373804444 1
0.1%
1.361614169 1
0.1%

altitude_gps
Real number (ℝ)

Distinct556
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean822.62999
Minimum0
Maximum865.60004
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:24.499911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile804.10004
Q1810.65364
median824.20001
Q3840.2102
95-th percentile860.70001
Maximum865.60004
Range865.60004
Interquartile range (IQR)29.55656

Descriptive statistics

Standard deviation65.670305
Coefficient of variation (CV)0.079829699
Kurtosis140.83256
Mean822.62999
Median Absolute Deviation (MAD)14.267441
Skewness-11.397733
Sum709929.68
Variance4312.5889
MonotonicityNot monotonic
2023-11-19T23:10:24.689291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
811.9000244 14
 
1.6%
860.7000122 12
 
1.4%
806.8000488 12
 
1.4%
830.4000244 11
 
1.3%
804.2000122 10
 
1.2%
811.7000122 8
 
0.9%
807 7
 
0.8%
856.5 7
 
0.8%
830.9000244 7
 
0.8%
804.5 7
 
0.8%
Other values (546) 768
89.0%
ValueCountFrequency (%)
0 5
0.6%
796.2391191 1
 
0.1%
797 1
 
0.1%
797.4083019 1
 
0.1%
798.4000244 1
 
0.1%
799.7044492 1
 
0.1%
800 2
 
0.2%
800.4000244 1
 
0.1%
800.6000366 3
0.3%
800.7000122 1
 
0.1%
ValueCountFrequency (%)
865.6000366 1
 
0.1%
864.4000244 1
 
0.1%
864.0095992 1
 
0.1%
863.6000366 2
 
0.2%
863.5 3
0.3%
863.4000244 1
 
0.1%
863.1000366 3
0.3%
863 5
0.6%
862.9000244 2
 
0.2%
862.3173342 1
 
0.1%

distancia_percorrida
Real number (ℝ)

UNIQUE 

Distinct863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3750457
Minimum0.00027121133
Maximum7.9370504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:24.865576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00027121133
5-th percentile0.081379944
Q11.2515406
median3.0505128
Q35.262519
95-th percentile7.4826882
Maximum7.9370504
Range7.9367792
Interquartile range (IQR)4.0109784

Descriptive statistics

Standard deviation2.3859963
Coefficient of variation (CV)0.7069523
Kurtosis-1.1557326
Mean3.3750457
Median Absolute Deviation (MAD)2.0346673
Skewness0.27593928
Sum2912.6644
Variance5.6929783
MonotonicityNot monotonic
2023-11-19T23:10:25.050262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0006496331111 1
 
0.1%
0.5977902148 1
 
0.1%
0.7544655172 1
 
0.1%
0.8034311495 1
 
0.1%
0.9364283669 1
 
0.1%
1.069581568 1
 
0.1%
1.121094082 1
 
0.1%
1.25254007 1
 
0.1%
1.310039859 1
 
0.1%
1.455702741 1
 
0.1%
Other values (853) 853
98.8%
ValueCountFrequency (%)
0.0002712113333 1
0.1%
0.0006496331111 1
0.1%
0.002004309556 1
0.1%
0.002131961278 1
0.1%
0.002985839083 1
0.1%
0.003011303194 1
0.1%
0.00308953025 1
0.1%
0.003885549778 1
0.1%
0.004624108806 1
0.1%
0.004631510694 1
0.1%
ValueCountFrequency (%)
7.937050429 1
0.1%
7.922852936 1
0.1%
7.884833549 1
0.1%
7.862314901 1
0.1%
7.851570028 1
0.1%
7.842430688 1
0.1%
7.832280071 1
0.1%
7.822790366 1
0.1%
7.820370555 1
0.1%
7.808888151 1
0.1%

distancia_percorrida_total
Real number (ℝ)

UNIQUE 

Distinct863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.416646
Minimum35.041872
Maximum42.978651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:25.250988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.041872
5-th percentile35.122981
Q136.293141
median38.092113
Q340.30412
95-th percentile42.524289
Maximum42.978651
Range7.9367792
Interquartile range (IQR)4.0109784

Descriptive statistics

Standard deviation2.3859963
Coefficient of variation (CV)0.062108396
Kurtosis-1.1557326
Mean38.416646
Median Absolute Deviation (MAD)2.0346673
Skewness0.27593928
Sum33153.566
Variance5.6929783
MonotonicityNot monotonic
2023-11-19T23:10:25.514535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.04225025 1
 
0.1%
35.63939083 1
 
0.1%
35.79606613 1
 
0.1%
35.84503177 1
 
0.1%
35.97802898 1
 
0.1%
36.11118219 1
 
0.1%
36.1626947 1
 
0.1%
36.29414069 1
 
0.1%
36.35164048 1
 
0.1%
36.49730336 1
 
0.1%
Other values (853) 853
98.8%
ValueCountFrequency (%)
35.04187183 1
0.1%
35.04225025 1
0.1%
35.04360493 1
0.1%
35.04373258 1
0.1%
35.04458646 1
0.1%
35.04461192 1
0.1%
35.04469015 1
0.1%
35.04548617 1
0.1%
35.04622473 1
0.1%
35.04623213 1
0.1%
ValueCountFrequency (%)
42.97865105 1
0.1%
42.96445355 1
0.1%
42.92643417 1
0.1%
42.90391552 1
0.1%
42.89317064 1
0.1%
42.88403131 1
0.1%
42.87388069 1
0.1%
42.86439098 1
0.1%
42.86197117 1
0.1%
42.85048877 1
0.1%

espaco_livre_no_tanque_combustivel
Real number (ℝ)

ZEROS 

Distinct69
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.113268
Minimum0
Maximum47.5
Zeros30
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:25.739191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.55
Q123
median26.5
Q338.5
95-th percentile43.5
Maximum47.5
Range47.5
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.8863754
Coefficient of variation (CV)0.33958316
Kurtosis0.67615859
Mean29.113268
Median Absolute Deviation (MAD)5.5
Skewness-0.45883014
Sum25124.75
Variance97.740418
MonotonicityNot monotonic
2023-11-19T23:10:25.944713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.5 50
 
5.8%
23 36
 
4.2%
24 36
 
4.2%
21 32
 
3.7%
25 30
 
3.5%
0 30
 
3.5%
25.5 29
 
3.4%
22 28
 
3.2%
26 26
 
3.0%
43 26
 
3.0%
Other values (59) 540
62.6%
ValueCountFrequency (%)
0 30
3.5%
9 1
 
0.1%
9.5 1
 
0.1%
12.5 2
 
0.2%
13 1
 
0.1%
14 2
 
0.2%
16 4
 
0.5%
16.5 3
 
0.3%
17 2
 
0.2%
17.5 1
 
0.1%
ValueCountFrequency (%)
47.5 1
 
0.1%
46.5 2
 
0.2%
46 3
 
0.3%
45.5 5
 
0.6%
45 9
 
1.0%
44.5 6
 
0.7%
44 15
1.7%
43.5 7
 
0.8%
43 26
3.0%
42.5 18
2.1%
Distinct69
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.773465
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:26.155169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q123
median47
Q354
95-th percentile66.9
Maximum100
Range95
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.772751
Coefficient of variation (CV)0.47333279
Kurtosis0.67615859
Mean41.773465
Median Absolute Deviation (MAD)11
Skewness0.45883014
Sum36050.5
Variance390.96167
MonotonicityNot monotonic
2023-11-19T23:10:26.309191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 50
 
5.8%
54 36
 
4.2%
52 36
 
4.2%
58 32
 
3.7%
50 30
 
3.5%
100 30
 
3.5%
49 29
 
3.4%
56 28
 
3.2%
48 26
 
3.0%
14 26
 
3.0%
Other values (59) 540
62.6%
ValueCountFrequency (%)
5 1
 
0.1%
7 2
 
0.2%
8 3
 
0.3%
9 5
 
0.6%
10 9
 
1.0%
11 6
 
0.7%
12 15
1.7%
13 7
 
0.8%
14 26
3.0%
15 18
2.1%
ValueCountFrequency (%)
100 30
3.5%
82 1
 
0.1%
81 1
 
0.1%
75 2
 
0.2%
74 1
 
0.1%
72 2
 
0.2%
68 4
 
0.5%
67 3
 
0.3%
66 2
 
0.2%
65 1
 
0.1%

nivel_combustivel_litros
Real number (ℝ)

Distinct69
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.886732
Minimum2.5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:26.454931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile6.5
Q111.5
median23.5
Q327
95-th percentile33.45
Maximum50
Range47.5
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.8863754
Coefficient of variation (CV)0.47333279
Kurtosis0.67615859
Mean20.886732
Median Absolute Deviation (MAD)5.5
Skewness0.45883014
Sum18025.25
Variance97.740418
MonotonicityNot monotonic
2023-11-19T23:10:26.603818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.5 50
 
5.8%
27 36
 
4.2%
26 36
 
4.2%
29 32
 
3.7%
25 30
 
3.5%
50 30
 
3.5%
24.5 29
 
3.4%
28 28
 
3.2%
24 26
 
3.0%
7 26
 
3.0%
Other values (59) 540
62.6%
ValueCountFrequency (%)
2.5 1
 
0.1%
3.5 2
 
0.2%
4 3
 
0.3%
4.5 5
 
0.6%
5 9
 
1.0%
5.5 6
 
0.7%
6 15
1.7%
6.5 7
 
0.8%
7 26
3.0%
7.5 18
2.1%
ValueCountFrequency (%)
50 30
3.5%
41 1
 
0.1%
40.5 1
 
0.1%
37.5 2
 
0.2%
37 1
 
0.1%
36 2
 
0.2%
34 4
 
0.5%
33.5 3
 
0.3%
33 2
 
0.2%
32.5 1
 
0.1%

posicao_acelerador_d
Real number (ℝ)

MISSING  ZEROS 

Distinct155
Distinct (%)18.6%
Missing31
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean25.526509
Minimum0
Maximum100
Zeros109
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:26.756889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.9411765
median20.588235
Q342.424242
95-th percentile70.098039
Maximum100
Range100
Interquartile range (IQR)39.483066

Descriptive statistics

Standard deviation24.456472
Coefficient of variation (CV)0.95808135
Kurtosis-0.16721161
Mean25.526509
Median Absolute Deviation (MAD)18.300654
Skewness0.80385851
Sum21238.056
Variance598.11904
MonotonicityNot monotonic
2023-11-19T23:10:26.907981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109
 
12.6%
2.941176471 65
 
7.5%
2.173913043 31
 
3.6%
2.777777778 29
 
3.4%
2.083333333 25
 
2.9%
50 18
 
2.1%
3.03030303 15
 
1.7%
3.571428571 15
 
1.7%
44.11764706 14
 
1.6%
32.35294118 13
 
1.5%
Other values (145) 498
57.7%
(Missing) 31
 
3.6%
ValueCountFrequency (%)
0 109
12.6%
1.785714286 2
 
0.2%
2.083333333 25
 
2.9%
2.173913043 31
 
3.6%
2.777777778 29
 
3.4%
2.941176471 65
7.5%
3.03030303 15
 
1.7%
3.571428571 15
 
1.7%
4.166666667 4
 
0.5%
4.347826087 1
 
0.1%
ValueCountFrequency (%)
100 7
0.8%
94.11764706 2
 
0.2%
93.93939394 1
 
0.1%
92.85714286 1
 
0.1%
91.66666667 1
 
0.1%
90.90909091 1
 
0.1%
89.28571429 1
 
0.1%
87.87878788 1
 
0.1%
85.71428571 2
 
0.2%
85.29411765 1
 
0.1%

posicao_acelerador_e
Real number (ℝ)

MISSING  ZEROS 

Distinct90
Distinct (%)10.8%
Missing31
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean25.24633
Minimum0
Maximum100
Zeros280
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:27.046984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21.214393
Q343.478261
95-th percentile68.75
Maximum100
Range100
Interquartile range (IQR)43.478261

Descriptive statistics

Standard deviation24.604523
Coefficient of variation (CV)0.97457819
Kurtosis-0.45045039
Mean25.24633
Median Absolute Deviation (MAD)21.214393
Skewness0.66911939
Sum21004.947
Variance605.38256
MonotonicityNot monotonic
2023-11-19T23:10:27.188982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 280
32.4%
50 26
 
3.0%
66.66666667 20
 
2.3%
61.11111111 17
 
2.0%
40 17
 
2.0%
38.88888889 17
 
2.0%
55.55555556 16
 
1.9%
25 15
 
1.7%
15 14
 
1.6%
17.39130435 12
 
1.4%
Other values (80) 398
46.1%
(Missing) 31
 
3.6%
ValueCountFrequency (%)
0 280
32.4%
3.448275862 1
 
0.1%
4.347826087 5
 
0.6%
5 5
 
0.6%
5.555555556 5
 
0.6%
5.882352941 5
 
0.6%
6.25 2
 
0.2%
6.666666667 1
 
0.1%
6.896551724 1
 
0.1%
8.695652174 3
 
0.3%
ValueCountFrequency (%)
100 6
0.7%
93.75 1
 
0.1%
93.33333333 1
 
0.1%
88.88888889 1
 
0.1%
87.5 1
 
0.1%
86.66666667 1
 
0.1%
83.33333333 1
 
0.1%
82.60869565 1
 
0.1%
82.35294118 1
 
0.1%
80 4
0.5%

rpm_motor
Real number (ℝ)

Distinct706
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2208.4612
Minimum613
Maximum4083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:27.324524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum613
5-th percentile909.2
Q11868
median2295
Q32642
95-th percentile3150.4
Maximum4083
Range3470
Interquartile range (IQR)774

Descriptive statistics

Standard deviation638.61003
Coefficient of variation (CV)0.28916516
Kurtosis-0.075401155
Mean2208.4612
Median Absolute Deviation (MAD)379
Skewness-0.37297404
Sum1905902
Variance407822.77
MonotonicityNot monotonic
2023-11-19T23:10:27.471933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
903 4
 
0.5%
2660 4
 
0.5%
2221 4
 
0.5%
905 4
 
0.5%
2119 3
 
0.3%
2037 3
 
0.3%
1906 3
 
0.3%
2508 3
 
0.3%
2238 3
 
0.3%
2557 3
 
0.3%
Other values (696) 829
96.1%
ValueCountFrequency (%)
613 1
0.1%
652 1
0.1%
788 1
0.1%
795 1
0.1%
796 1
0.1%
797 1
0.1%
800 1
0.1%
807 1
0.1%
813 1
0.1%
821 1
0.1%
ValueCountFrequency (%)
4083 1
0.1%
4038 1
0.1%
3915 1
0.1%
3880 1
0.1%
3826 1
0.1%
3697 1
0.1%
3628 1
0.1%
3586 1
0.1%
3566 1
0.1%
3552 1
0.1%

temperatura_arrefecimento
Real number (ℝ)

Distinct65
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.987254
Minimum26
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:27.610085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile46
Q181
median84
Q386
95-th percentile88
Maximum92
Range66
Interquartile range (IQR)5

Descriptive statistics

Standard deviation13.139571
Coefficient of variation (CV)0.16635052
Kurtosis4.0270457
Mean78.987254
Median Absolute Deviation (MAD)2
Skewness-2.1692544
Sum68166
Variance172.64833
MonotonicityNot monotonic
2023-11-19T23:10:28.171582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 145
16.8%
85 102
11.8%
86 96
11.1%
83 75
 
8.7%
87 62
 
7.2%
88 57
 
6.6%
82 48
 
5.6%
81 26
 
3.0%
89 25
 
2.9%
80 11
 
1.3%
Other values (55) 216
25.0%
ValueCountFrequency (%)
26 2
 
0.2%
27 1
 
0.1%
28 2
 
0.2%
31 2
 
0.2%
32 2
 
0.2%
33 5
0.6%
34 2
 
0.2%
35 2
 
0.2%
36 3
0.3%
37 3
0.3%
ValueCountFrequency (%)
92 2
 
0.2%
91 5
 
0.6%
90 10
 
1.2%
89 25
 
2.9%
88 57
 
6.6%
87 62
7.2%
86 96
11.1%
85 102
11.8%
84 145
16.8%
83 75
8.7%

velocidade_gps
Real number (ℝ)

ZEROS 

Distinct812
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.217541
Minimum0
Maximum61.897776
Zeros52
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:28.342372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.539633
median31.774569
Q339.151646
95-th percentile46.916125
Maximum61.897776
Range61.897776
Interquartile range (IQR)17.612014

Descriptive statistics

Standard deviation13.569655
Coefficient of variation (CV)0.46443523
Kurtosis-0.28795962
Mean29.217541
Median Absolute Deviation (MAD)8.4567261
Skewness-0.63999654
Sum25214.738
Variance184.13554
MonotonicityNot monotonic
2023-11-19T23:10:28.488862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
6.0%
41.44515038 1
 
0.1%
12.81971455 1
 
0.1%
36.579776 1
 
0.1%
31.57550011 1
 
0.1%
37.29305992 1
 
0.1%
40.73064766 1
 
0.1%
38.5129509 1
 
0.1%
43.37894669 1
 
0.1%
23.8649929 1
 
0.1%
Other values (802) 802
92.9%
ValueCountFrequency (%)
0 52
6.0%
1.514040413 × 10-81
 
0.1%
2.001251005 × 10-81
 
0.1%
1.353543366 × 10-51
 
0.1%
0.4117742032 1
 
0.1%
1.155091596 1
 
0.1%
1.220038068 1
 
0.1%
1.284507322 1
 
0.1%
1.860028696 1
 
0.1%
1.880317998 1
 
0.1%
ValueCountFrequency (%)
61.89777603 1
0.1%
60.48392487 1
0.1%
57.21712646 1
0.1%
56.43985405 1
0.1%
55.76037712 1
0.1%
54.13435593 1
0.1%
54.11644478 1
0.1%
53.69121208 1
0.1%
52.73068771 1
0.1%
52.61319237 1
0.1%

velocidade_veiculo
Real number (ℝ)

Distinct60
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.232329
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:28.629093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q124
median33
Q340
95-th percentile47
Maximum62
Range61
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.605547
Coefficient of variation (CV)0.37158761
Kurtosis-0.10868688
Mean31.232329
Median Absolute Deviation (MAD)7
Skewness-0.56035387
Sum26953.5
Variance134.68871
MonotonicityNot monotonic
2023-11-19T23:10:28.772301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 38
 
4.4%
38 36
 
4.2%
39 36
 
4.2%
34 35
 
4.1%
43 34
 
3.9%
35 34
 
3.9%
36 33
 
3.8%
41 31
 
3.6%
32 31
 
3.6%
31 29
 
3.4%
Other values (50) 526
61.0%
ValueCountFrequency (%)
1 2
 
0.2%
2 9
1.0%
3 4
 
0.5%
4 4
 
0.5%
5 7
0.8%
6 6
0.7%
7 10
1.2%
8 8
0.9%
9 4
 
0.5%
10 11
1.3%
ValueCountFrequency (%)
62 1
 
0.1%
61 1
 
0.1%
57 1
 
0.1%
56 2
 
0.2%
55 1
 
0.1%
54 2
 
0.2%
53 1
 
0.1%
52 4
0.5%
51 5
0.6%
50 6
0.7%

velocidade_media
Real number (ℝ)

UNIQUE 

Distinct863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.263145
Minimum0.047916796
Maximum30.191086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:28.915409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.047916796
5-th percentile6.5102415
Q120.34491
median24.299453
Q326.732126
95-th percentile28.965772
Maximum30.191086
Range30.14317
Interquartile range (IQR)6.3872157

Descriptive statistics

Standard deviation6.6217647
Coefficient of variation (CV)0.29743169
Kurtosis1.9054726
Mean22.263145
Median Absolute Deviation (MAD)2.7475431
Skewness-1.5381717
Sum19213.094
Variance43.847767
MonotonicityNot monotonic
2023-11-19T23:10:29.065414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1544066771 1
 
0.1%
16.6945332 1
 
0.1%
18.32548175 1
 
0.1%
18.91182384 1
 
0.1%
20.04914787 1
 
0.1%
20.9996979 1
 
0.1%
21.49605645 1
 
0.1%
22.22702088 1
 
0.1%
22.63915269 1
 
0.1%
23.45536486 1
 
0.1%
Other values (853) 853
98.8%
ValueCountFrequency (%)
0.04791679622 1
0.1%
0.1542560433 1
0.1%
0.1544066771 1
0.1%
0.1970782476 1
0.1%
0.2964382517 1
0.1%
0.4499962448 1
0.1%
0.5524385583 1
0.1%
0.6366523129 1
0.1%
0.6440260996 1
0.1%
0.8177531561 1
0.1%
ValueCountFrequency (%)
30.19108648 1
0.1%
30.08135217 1
0.1%
30.01671011 1
0.1%
29.75359425 1
0.1%
29.6424061 1
0.1%
29.63163518 1
0.1%
29.57215321 1
0.1%
29.55944224 1
0.1%
29.55159841 1
0.1%
29.53527567 1
0.1%

velocidade_media_gps
Real number (ℝ)

UNIQUE 

Distinct863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.81093
Minimum0.0082614605
Maximum26.628141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:29.206302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0082614605
5-th percentile4.5320126
Q116.945317
median20.597241
Q322.736729
95-th percentile24.826371
Maximum26.628141
Range26.61988
Interquartile range (IQR)5.7914114

Descriptive statistics

Standard deviation5.9162425
Coefficient of variation (CV)0.31451089
Kurtosis1.5673849
Mean18.81093
Median Absolute Deviation (MAD)2.4733465
Skewness-1.4619569
Sum16233.833
Variance35.001926
MonotonicityNot monotonic
2023-11-19T23:10:29.358303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2838891076 1
 
0.1%
13.77658161 1
 
0.1%
14.78546986 1
 
0.1%
15.07680301 1
 
0.1%
16.17453093 1
 
0.1%
17.0453866 1
 
0.1%
17.40490508 1
 
0.1%
18.02255121 1
 
0.1%
18.27790429 1
 
0.1%
18.75992354 1
 
0.1%
Other values (853) 853
98.8%
ValueCountFrequency (%)
0.008261460494 1
0.1%
0.01950218675 1
0.1%
0.06715579141 1
0.1%
0.139815058 1
0.1%
0.279040719 1
0.1%
0.2838891076 1
0.1%
0.2873034391 1
0.1%
0.3314060229 1
0.1%
0.3783178376 1
0.1%
0.6298663066 1
0.1%
ValueCountFrequency (%)
26.62814115 1
0.1%
26.55407547 1
0.1%
26.50318955 1
0.1%
26.25877227 1
0.1%
26.14006361 1
0.1%
26.13898398 1
0.1%
26.1273699 1
0.1%
26.1147199 1
0.1%
26.10412072 1
0.1%
26.08858724 1
0.1%

latitude_gps
Real number (ℝ)

UNIQUE 

Distinct863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-23.547518
Minimum-23.556434
Maximum-23.533336
Zeros0
Zeros (%)0.0%
Negative863
Negative (%)100.0%
Memory size13.5 KiB
2023-11-19T23:10:29.506397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23.556434
5-th percentile-23.556114
Q1-23.553024
median-23.549179
Q3-23.541655
95-th percentile-23.534594
Maximum-23.533336
Range0.023098071
Interquartile range (IQR)0.01136908

Descriptive statistics

Standard deviation0.0067999472
Coefficient of variation (CV)-0.00028877554
Kurtosis-0.9610189
Mean-23.547518
Median Absolute Deviation (MAD)0.0046449306
Skewness0.55638327
Sum-20321.508
Variance4.6239282 × 10-5
MonotonicityNot monotonic
2023-11-19T23:10:29.654306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-23.55627408 1
 
0.1%
-23.55323659 1
 
0.1%
-23.55291446 1
 
0.1%
-23.55285273 1
 
0.1%
-23.55281885 1
 
0.1%
-23.55289021 1
 
0.1%
-23.55292579 1
 
0.1%
-23.55285085 1
 
0.1%
-23.55267741 1
 
0.1%
-23.55186878 1
 
0.1%
Other values (853) 853
98.8%
ValueCountFrequency (%)
-23.55643438 1
0.1%
-23.55641692 1
0.1%
-23.55641043 1
0.1%
-23.556402 1
0.1%
-23.55637585 1
0.1%
-23.5563568 1
0.1%
-23.55634134 1
0.1%
-23.55633701 1
0.1%
-23.55633618 1
0.1%
-23.55633099 1
0.1%
ValueCountFrequency (%)
-23.53333631 1
0.1%
-23.53333896 1
0.1%
-23.53335659 1
0.1%
-23.53337375 1
0.1%
-23.53337665 1
0.1%
-23.53338155 1
0.1%
-23.53338875 1
0.1%
-23.53340495 1
0.1%
-23.53341568 1
0.1%
-23.53344247 1
0.1%

longitude_gps
Real number (ℝ)

UNIQUE 

Distinct863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-46.888873
Minimum-46.898512
Maximum-46.880136
Zeros0
Zeros (%)0.0%
Negative863
Negative (%)100.0%
Memory size13.5 KiB
2023-11-19T23:10:29.799051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-46.898512
5-th percentile-46.89813
Q1-46.896015
median-46.887656
Q3-46.884204
95-th percentile-46.880389
Maximum-46.880136
Range0.018375747
Interquartile range (IQR)0.011810662

Descriptive statistics

Standard deviation0.0062964304
Coefficient of variation (CV)-0.00013428411
Kurtosis-1.467013
Mean-46.888873
Median Absolute Deviation (MAD)0.0057754693
Skewness-0.19732426
Sum-40465.097
Variance3.9645036 × 10-5
MonotonicityNot monotonic
2023-11-19T23:10:29.948186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-46.8961167 1
 
0.1%
-46.89757181 1
 
0.1%
-46.89595203 1
 
0.1%
-46.89544903 1
 
0.1%
-46.89413771 1
 
0.1%
-46.89285105 1
 
0.1%
-46.89228455 1
 
0.1%
-46.89099547 1
 
0.1%
-46.89057316 1
 
0.1%
-46.88940982 1
 
0.1%
Other values (853) 853
98.8%
ValueCountFrequency (%)
-46.89851152 1
0.1%
-46.8984493 1
0.1%
-46.89841249 1
0.1%
-46.89840704 1
0.1%
-46.89840004 1
0.1%
-46.8983996 1
0.1%
-46.89839495 1
0.1%
-46.89838996 1
0.1%
-46.89838775 1
0.1%
-46.8983501 1
0.1%
ValueCountFrequency (%)
-46.88013578 1
0.1%
-46.88014933 1
0.1%
-46.88015171 1
0.1%
-46.88015269 1
0.1%
-46.88015404 1
0.1%
-46.88016892 1
0.1%
-46.88022583 1
0.1%
-46.88022773 1
0.1%
-46.8802282 1
0.1%
-46.88024986 1
0.1%

file
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
data_1.csv
795 
data_2.csv
 
68

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8630
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdata_1.csv
2nd rowdata_1.csv
3rd rowdata_1.csv
4th rowdata_1.csv
5th rowdata_1.csv

Common Values

ValueCountFrequency (%)
data_1.csv 795
92.1%
data_2.csv 68
 
7.9%

Length

2023-11-19T23:10:30.082185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-19T23:10:30.186085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
data_1.csv 795
92.1%
data_2.csv 68
 
7.9%

Most occurring characters

ValueCountFrequency (%)
a 1726
20.0%
d 863
10.0%
t 863
10.0%
_ 863
10.0%
. 863
10.0%
c 863
10.0%
s 863
10.0%
v 863
10.0%
1 795
9.2%
2 68
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6041
70.0%
Connector Punctuation 863
 
10.0%
Other Punctuation 863
 
10.0%
Decimal Number 863
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1726
28.6%
d 863
14.3%
t 863
14.3%
c 863
14.3%
s 863
14.3%
v 863
14.3%
Decimal Number
ValueCountFrequency (%)
1 795
92.1%
2 68
 
7.9%
Connector Punctuation
ValueCountFrequency (%)
_ 863
100.0%
Other Punctuation
ValueCountFrequency (%)
. 863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6041
70.0%
Common 2589
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1726
28.6%
d 863
14.3%
t 863
14.3%
c 863
14.3%
s 863
14.3%
v 863
14.3%
Common
ValueCountFrequency (%)
_ 863
33.3%
. 863
33.3%
1 795
30.7%
2 68
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1726
20.0%
d 863
10.0%
t 863
10.0%
_ 863
10.0%
. 863
10.0%
c 863
10.0%
s 863
10.0%
v 863
10.0%
1 795
9.2%
2 68
 
0.8%

aceleracao_calculada
Real number (ℝ)

Distinct29
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.792584
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:30.291184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q311
95-th percentile19
Maximum28
Range27
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.640771
Coefficient of variation (CV)0.723864
Kurtosis0.41401845
Mean7.792584
Median Absolute Deviation (MAD)4
Skewness0.94488991
Sum6725
Variance31.818298
MonotonicityNot monotonic
2023-11-19T23:10:30.426584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 83
 
9.6%
3 82
 
9.5%
2 73
 
8.5%
4 65
 
7.5%
6 65
 
7.5%
7 64
 
7.4%
5 62
 
7.2%
8 44
 
5.1%
9 44
 
5.1%
11 38
 
4.4%
Other values (19) 243
28.2%
ValueCountFrequency (%)
1 83
9.6%
2 73
8.5%
3 82
9.5%
4 65
7.5%
5 62
7.2%
6 65
7.5%
7 64
7.4%
8 44
5.1%
8.5 2
 
0.2%
9 44
5.1%
ValueCountFrequency (%)
28 1
 
0.1%
27 2
 
0.2%
26 4
 
0.5%
25 1
 
0.1%
24 5
 
0.6%
23 1
 
0.1%
22 4
 
0.5%
21 6
0.7%
20 10
1.2%
19 13
1.5%
Distinct29
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.792584
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.5 KiB
2023-11-19T23:10:30.548688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q311
95-th percentile19
Maximum28
Range27
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.640771
Coefficient of variation (CV)0.723864
Kurtosis0.41401845
Mean7.792584
Median Absolute Deviation (MAD)4
Skewness0.94488991
Sum6725
Variance31.818298
MonotonicityNot monotonic
2023-11-19T23:10:30.725904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 83
 
9.6%
3 82
 
9.5%
2 73
 
8.5%
4 65
 
7.5%
6 65
 
7.5%
7 64
 
7.4%
5 62
 
7.2%
8 44
 
5.1%
9 44
 
5.1%
11 38
 
4.4%
Other values (19) 243
28.2%
ValueCountFrequency (%)
1 83
9.6%
2 73
8.5%
3 82
9.5%
4 65
7.5%
5 62
7.2%
6 65
7.5%
7 64
7.4%
8 44
5.1%
8.5 2
 
0.2%
9 44
5.1%
ValueCountFrequency (%)
28 1
 
0.1%
27 2
 
0.2%
26 4
 
0.5%
25 1
 
0.1%
24 5
 
0.6%
23 1
 
0.1%
22 4
 
0.5%
21 6
0.7%
20 10
1.2%
19 13
1.5%

driver
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
driver_b
344 
driver_c
308 
driver_a
211 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6904
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdriver_a
2nd rowdriver_a
3rd rowdriver_a
4th rowdriver_a
5th rowdriver_a

Common Values

ValueCountFrequency (%)
driver_b 344
39.9%
driver_c 308
35.7%
driver_a 211
24.4%

Length

2023-11-19T23:10:30.866626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-19T23:10:30.979167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
driver_b 344
39.9%
driver_c 308
35.7%
driver_a 211
24.4%

Most occurring characters

ValueCountFrequency (%)
r 1726
25.0%
d 863
12.5%
i 863
12.5%
v 863
12.5%
e 863
12.5%
_ 863
12.5%
b 344
 
5.0%
c 308
 
4.5%
a 211
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6041
87.5%
Connector Punctuation 863
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1726
28.6%
d 863
14.3%
i 863
14.3%
v 863
14.3%
e 863
14.3%
b 344
 
5.7%
c 308
 
5.1%
a 211
 
3.5%
Connector Punctuation
ValueCountFrequency (%)
_ 863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6041
87.5%
Common 863
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1726
28.6%
d 863
14.3%
i 863
14.3%
v 863
14.3%
e 863
14.3%
b 344
 
5.7%
c 308
 
5.1%
a 211
 
3.5%
Common
ValueCountFrequency (%)
_ 863
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1726
25.0%
d 863
12.5%
i 863
12.5%
v 863
12.5%
e 863
12.5%
_ 863
12.5%
b 344
 
5.0%
c 308
 
4.5%
a 211
 
3.1%

route
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
route_a
863 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6041
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowroute_a
2nd rowroute_a
3rd rowroute_a
4th rowroute_a
5th rowroute_a

Common Values

ValueCountFrequency (%)
route_a 863
100.0%

Length

2023-11-19T23:10:31.182691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-19T23:10:31.370107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
route_a 863
100.0%

Most occurring characters

ValueCountFrequency (%)
r 863
14.3%
o 863
14.3%
u 863
14.3%
t 863
14.3%
e 863
14.3%
_ 863
14.3%
a 863
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5178
85.7%
Connector Punctuation 863
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 863
16.7%
o 863
16.7%
u 863
16.7%
t 863
16.7%
e 863
16.7%
a 863
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5178
85.7%
Common 863
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 863
16.7%
o 863
16.7%
u 863
16.7%
t 863
16.7%
e 863
16.7%
a 863
16.7%
Common
ValueCountFrequency (%)
_ 863
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 863
14.3%
o 863
14.3%
u 863
14.3%
t 863
14.3%
e 863
14.3%
_ 863
14.3%
a 863
14.3%

trip
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
trip_2
325 
trip_1
323 
trip_3
215 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5178
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrip_1
2nd rowtrip_1
3rd rowtrip_1
4th rowtrip_1
5th rowtrip_1

Common Values

ValueCountFrequency (%)
trip_2 325
37.7%
trip_1 323
37.4%
trip_3 215
24.9%

Length

2023-11-19T23:10:31.572850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-19T23:10:31.732461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
trip_2 325
37.7%
trip_1 323
37.4%
trip_3 215
24.9%

Most occurring characters

ValueCountFrequency (%)
t 863
16.7%
r 863
16.7%
i 863
16.7%
p 863
16.7%
_ 863
16.7%
2 325
 
6.3%
1 323
 
6.2%
3 215
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3452
66.7%
Connector Punctuation 863
 
16.7%
Decimal Number 863
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 863
25.0%
r 863
25.0%
i 863
25.0%
p 863
25.0%
Decimal Number
ValueCountFrequency (%)
2 325
37.7%
1 323
37.4%
3 215
24.9%
Connector Punctuation
ValueCountFrequency (%)
_ 863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3452
66.7%
Common 1726
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 863
25.0%
r 863
25.0%
i 863
25.0%
p 863
25.0%
Common
ValueCountFrequency (%)
_ 863
50.0%
2 325
 
18.8%
1 323
 
18.7%
3 215
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 863
16.7%
r 863
16.7%
i 863
16.7%
p 863
16.7%
_ 863
16.7%
2 325
 
6.3%
1 323
 
6.2%
3 215
 
4.2%

Interactions

2023-11-19T23:10:20.976562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:36.301386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:38.776421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:40.739301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:43.512567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:45.657950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:48.019263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.361943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:52.656705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:54.839591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:57.173647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:00.007546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:02.694237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:05.564586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:07.613639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:10.114039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:12.733056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:15.820684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:18.482942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:21.120800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:36.428450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:38.887438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:40.855922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:43.642338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:45.764392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:48.160263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.483530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:52.764925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:54.933160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:57.383237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:00.134899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:02.848189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:05.673586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:07.718556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:10.243555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:12.876146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:15.970853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:18.635764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:21.230171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:36.549922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:38.980515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:40.960330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:43.746752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:45.868503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:48.280961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.576566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:52.866924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:55.019990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:57.581665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:00.246151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:02.967150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:05.774096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:07.810941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:10.366650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:12.993293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:16.105412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:18.766017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:21.339522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:36.663261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:39.084771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:41.076783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:43.863751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:45.985638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:48.414716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.685571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:52.978929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:55.139393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:57.850454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:00.393265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:03.118151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:05.886664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:07.916229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:10.528463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:13.138885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:16.253700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:18.923013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:21.450023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:36.786815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:39.196238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:41.236049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:43.978735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:46.095532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:48.536724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.804569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:53.091930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:55.249397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:58.030596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:00.560778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:03.587956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:06.001967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:08.059362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:10.690563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:13.270730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:16.407077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:19.057925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:21.550733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:36.918815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:39.302515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:41.351841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:44.086509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:46.195887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:48.651825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.902787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:53.189472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:55.340724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:58.165328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:00.708675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:03.724159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:06.098968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:08.160378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:10.827416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:13.405246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:16.553604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:19.189585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:21.658393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:37.045422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-11-19T23:10:02.247504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:05.249349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:07.294934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:09.697461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:12.288945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:15.382307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:18.060311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:20.560446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:23.022483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:38.560231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:40.536537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:43.295297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:45.434490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:47.753146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.104674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:52.430000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:54.677491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:56.925159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:59.736316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:02.395983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:05.364969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:07.404401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:09.858622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:12.449237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:15.533850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:18.211494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:20.714240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:23.154661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:38.665421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:40.631837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:43.403563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:45.544440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:47.898762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:50.238473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:52.552615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:54.759589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:57.047544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:09:59.881010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:02.539033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:05.464193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:07.505558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:09.988909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:12.586070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:15.681715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:18.340962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-19T23:10:20.848736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-11-19T23:10:23.374295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-19T23:10:23.777749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-19T23:10:23.954686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

aceleracaoaltitude_gpsdistancia_percorridadistancia_percorrida_totalespaco_livre_no_tanque_combustivelnivel_combustivel_porcentagemnivel_combustivel_litrosposicao_acelerador_dposicao_acelerador_erpm_motortemperatura_arrefecimentovelocidade_gpsvelocidade_veiculovelocidade_mediavelocidade_media_gpslatitude_gpslongitude_gpsfileaceleracao_calculadaaceleracao_calculada_absolutadriverroutetrip
time
1900-01-01 19:32:400.127958808.6628640.00065035.04225039.022.011.02.1739130.0796.087.00.0000002.00.1544070.283889-23.556274-46.896117data_1.csv2.02.0driver_aroute_atrip_1
1900-01-01 19:32:450.5124200.0000000.00877735.05037839.022.011.02.1739130.0821.087.00.0000007.01.6256801.146480-23.556272-46.896038data_1.csv5.05.0driver_aroute_atrip_1
1900-01-01 19:32:500.519096810.4616150.02641535.06801641.517.08.52.1739130.0889.087.012.65772611.03.8481611.848885-23.556337-46.895878data_1.csv4.04.0driver_aroute_atrip_1
1900-01-01 19:33:00-0.127850804.7207330.04951835.09111838.523.011.52.1739130.0834.087.08.0908117.05.1595323.007439-23.556434-46.895662data_1.csv1.01.0driver_aroute_atrip_1
1900-01-01 19:33:100.193700805.8547190.06860135.11020242.515.07.523.91304325.01857.088.09.86844610.05.5984203.580662-23.556341-46.895733data_1.csv3.03.0driver_aroute_atrip_1
1900-01-01 19:33:200.512840809.0000000.09896735.14056842.016.08.013.04347820.01128.088.012.15273511.06.4861964.303328-23.556270-46.896022data_1.csv2.02.0driver_aroute_atrip_1
1900-01-01 19:33:250.518221811.0787970.11640735.15800844.012.06.056.52173965.02050.088.019.30716519.07.0767395.023269-23.556206-46.896144data_1.csv8.08.0driver_aroute_atrip_1
1900-01-01 19:33:300.384827815.1000370.15796035.19956044.012.06.036.95652245.02911.089.028.59964429.08.7856066.354345-23.556114-46.896550data_1.csv10.010.0driver_aroute_atrip_1
1900-01-01 19:33:450.647747828.5000000.25362735.29522843.513.06.510.8695650.02221.088.025.75454726.011.4432768.557102-23.555595-46.897061data_1.csv9.09.0driver_aroute_atrip_1
1900-01-01 19:33:550.840703828.2000120.30567335.34727443.014.07.056.52173960.01726.089.025.87503126.012.3092349.456901-23.555113-46.896960data_1.csv13.013.0driver_aroute_atrip_1
aceleracaoaltitude_gpsdistancia_percorridadistancia_percorrida_totalespaco_livre_no_tanque_combustivelnivel_combustivel_porcentagemnivel_combustivel_litrosposicao_acelerador_dposicao_acelerador_erpm_motortemperatura_arrefecimentovelocidade_gpsvelocidade_veiculovelocidade_mediavelocidade_media_gpslatitude_gpslongitude_gpsfileaceleracao_calculadaaceleracao_calculada_absolutadriverroutetrip
time
1900-01-01 21:40:350.867994856.4826857.42434442.46594538.024.012.02.9411760.000000911.082.012.90320015.029.31181823.797967-23.553362-46.898242data_1.csv15.015.0driver_croute_atrip_3
1900-01-01 21:40:400.465774851.9000247.45252642.49412641.018.09.02.9411760.000000903.082.021.49646422.029.25218323.744114-23.553434-46.898321data_1.csv7.07.0driver_croute_atrip_3
1900-01-01 21:40:500.395133843.1000377.51447642.55607732.036.018.02.9411760.000000906.082.027.83601528.029.18623523.692587-23.553871-46.898180data_1.csv9.09.0driver_croute_atrip_3
1900-01-01 21:41:000.633100837.4000247.56742142.60902134.531.015.52.9411760.000000900.082.022.42232720.029.07919423.608731-23.554024-46.897644data_1.csv7.07.0driver_croute_atrip_3
1900-01-01 21:41:051.163751835.1219817.59164442.63324536.527.013.555.88235355.5555561383.082.020.73120625.029.00673923.550045-23.554001-46.897444data_1.csv5.05.0driver_croute_atrip_3
1900-01-01 21:41:100.000000837.1000377.62716442.66876543.513.06.52.9411760.0000001107.081.027.36281529.029.00771723.566048-23.554248-46.897130data_1.csv4.04.0driver_croute_atrip_3
1900-01-01 21:41:150.231945833.7650477.67557742.71717737.026.013.02.9411760.000000926.081.032.24487334.029.02629123.573922-23.554613-46.897023data_1.csv5.05.0driver_croute_atrip_3
1900-01-01 21:41:301.169784830.9000247.78031442.82191536.527.013.52.9411760.0000002112.080.026.98283426.028.95857723.527568-23.555598-46.897098data_1.csv14.014.0driver_croute_atrip_3
1900-01-01 21:41:400.693748825.6000377.85157042.89317132.036.018.02.9411760.000000931.080.032.20353034.028.93103723.501047-23.556073-46.896819data_1.csv12.012.0driver_croute_atrip_3
1900-01-01 21:42:00-0.551899801.0202027.93705042.97865143.014.07.032.35294127.7777781585.081.06.6180838.028.74013923.302372-23.556417-46.896015data_1.csv3.03.0driver_croute_atrip_3